Machine Type Communications (MTC) are expected to contribute heavily to connectivity and traffic within the mobile broadband industry. The Global System for Mobile Communications/Enhanced Data Rates for GSM Evolution (GSM/EDGE) system already serves a rapidly expanding market for MTC. Mobile communications operators have expressed interest in accommodating traffic that serves wireless sensors/devices within modern evolved networks such as those based on Long Term Evolution (LTE). As part of this, it would be incumbent on them to handle MTC traffic served by existing cellular networks such as GSM/EDGE and to provide a transition for such traffic from e.g., General Packet Radio Services/EDGE (GPRS/EDGE) to future versions of cellular systems, such as 3GPP Long Term Evolution Advance (LTE-A, or LTE-Advanced).
Wireless sensor networks have gained increasing interest from academia and industry. Such networks have, however, predominantly been built around short range communication links, such as those based on Bluetooth, and more recently on the Zigbee standard. It is of particular interest to examine whether existing and future cellular systems can be modified to efficiently accommodate the traffic from these wireless sensor devices. This is a challenging task considering that (1) the latest versions of existing cellular systems, 3GPP systems, such as High Speed Packet Access (HSPA), Long Term Evolution (LTE), or LTE-A, or Institute of Electrical and Electronics Engineers (IEEE) systems, such as 802.16 (WiMax), are conceived primarily with the goal of providing service mainly to mobile broadband users and (2) there is a requirement from operators that these wireless devices (sensors) are low cost and have high energy efficiency.
Signaling mechanisms in existing and future 3GPP and IEEE networks have been conceived with the intention of securing a robust connection/session lasting for long periods of time, and involving transmission of large data volumes. In this respect, signaling mechanisms and protocols involving several long messages amounting to hundreds or thousands of kilobytes of data are not considered as particularly significant overhead when compared to the amount of data traffic exchanged within a session.
However, many wireless sensor devices are expected to transmit with very low activity and with long periods of inactivity between transmissions. Also, such devices typically transmit small amounts of information—typically a few hundred octets of data, indicating, e.g., a measurement or presence. Some wireless sensor devices serve as actuating receivers, where a short message from the network of a few hundred octets of data may need to be processed and acted on. The existing signaling mechanisms for establishing and maintaining a connection with such devices are considered as being considerably “heavy” for such device types or application categories, and there is a real concern that the volume of signaling traffic can quickly overwhelm the cellular network. In other words, the signaling overhead is no longer negligible for very small transmissions. In addition, keeping a connection up or reestablishing a connection on wake-up may constitute an undue burden on a device with a targeted battery life that spans years.
In the most common scenario, devices are anticipated to transmit in uplink a single packet containing measurements, warnings, or other types of information to the cellular network. Hence, data transmissions occur mainly in the uplink, while the downlink serves mainly for transmitting feedback and link control information to devices.
In this respect, entire radio network interfaces and radio resource management algorithms require new approaches. However, in order to perform these modifications to radio protocol architectures and to radio resource management, there is a need to have information on the network side regarding some characteristics of machine devices related to their capabilities, including, for example, their mobility pattern, energy supply and traffic pattern.
Merely polling the machine devices to access device characteristics and capabilities may be challenging because such information is not always hard-coded at the device. Also, the devices might change regarding one of these characteristics, i.e. either their mobility pattern or traffic pattern, or even their energy profile. One example is that the environment related to such information (e.g., mobility pattern) varies with time. Another example is that the device is originally designed for general purposes but is then installed in a specific environment (e.g., mobility pattern).
Accordingly, there is a need for a method and device for adjusting resource management procedures based on machine device capability information, including, for example, mobility pattern information.